The Unbearable Lightness of Health Science Reporting: A Week Examining Italian Print Media

BACKGROUND: Although being an important source of science news information to the public, print news media have often been criticized in their credibility. Health-related content of press media articles has been examined by many studies underlining that information about benefits, risks and costs are often incomplete or inadequate and financial conflicts of interest are rarely reported. However, these studies have focused their analysis on very selected science articles. The present research aimed at adopting a wider explorative approach, by analysing all types of health science information appearing on the Italian national press in one-week period. Moreover, we attempted to score the balance of the articles. METHODOLOGY/PRINCIPAL FINDINGS: We collected 146 health science communication articles defined as articles aiming at improving the reader's knowledge on health from a scientific perspective. Articles were evaluated by 3 independent physicians with respect to different divulgation parameters: benefits, costs, risks, sources of information, disclosure of financial conflicts of interest and balance. Balance was evaluated with regard to exaggerated or non correct claims. The selected articles appeared on 41 Italian national daily newspapers and 41 weekly magazines, representing 89% of national circulation copies: 97 articles (66%) covered common medical treatments or basic scientific research and 49 (34%) were about new medical treatments, procedures, tests or products. We found that only 6/49 (12%) articles on new treatments, procedures, tests or products mentioned costs or risks to patients. Moreover, benefits were always maximized and in 16/49 cases (33%) they were presented in relative rather than absolute terms. The majority of stories (133/146, 91%) did not report any financial conflict of interest. Among these, 15 were shown to underreport them (15/146, 9.5%), as we demonstrated that conflicts of interest did actually exist. Unbalanced articles were 27/146 (18%). Specifically, the probability of unbalanced reporting was significantly increased in stories about a new treatment, procedure, test or product (22/49, 45%), compared to stories covering common treatments or basic scientific research (5/97, 5%) (risk ratio, 8.72). CONCLUSIONS/SIGNIFICANCE: Consistent with prior research on health science communication in other countries, we report undisclosed costs and risks, emphasized benefits, unrevealed financial conflicts of interest and exaggerated claims in Italian print media. In addition, we show that the risk for a story about a new medical approach to be unbalanced is almost 9 times higher with respect to stories about any other kind of health science-related topics. These findings raise again the fundamental issue whether popular media is detrimental rather than useful to public health

Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter

Background: Traditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients, relying instead on gatekeepers, such as clinicians and drug safety groups, to verify each potential event. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines. Objective: The aim of the study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency. Methods: We collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA®). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC). Results: Of the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 72 % recall and 86 % precision. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 (p < 0.0001) between Proto-AEs reported in Twitter and FAERS by SOC. Conclusion: Patients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation

Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter

BackgroundTraditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients, relying instead on gatekeepers, such as clinicians and drug safety groups, to verify each potential event. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines.ObjectiveThe aim of the study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency.MethodsWe collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA®). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC).ResultsOf the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 72% recall and 86% precision. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 (p<0.0001) between Proto-AEs reported in Twitter and FAERS by SOC.ConclusionPatients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation

A Generalizable Deep Learning System for Cardiac MRI

Cardiac MRI allows for a comprehensive assessment of myocardial structure, function, and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep learning model is trained via self-supervised contrastive learning, by which visual concepts in cine-sequence cardiac MRI scans are learned from the raw text of the accompanying radiology reports. We train and evaluate our model on data from four large academic clinical institutions in the United States. We additionally showcase the performance of our models on the UK BioBank, and two additional publicly available external datasets. We explore emergent zero-shot capabilities of our system, and demonstrate remarkable performance across a range of tasks; including the problem of left ventricular ejection fraction regression, and the diagnosis of 35 different conditions such as cardiac amyloidosis and hypertrophic cardiomyopathy. We show that our deep learning system is capable of not only understanding the staggering complexity of human cardiovascular disease, but can be directed towards clinical problems of interest yielding impressive, clinical grade diagnostic accuracy with a fraction of the training data typically required for such tasks.Comment: 21 page main manuscript, 4 figures. Supplementary Appendix and code will be made available on publicatio

Circulating microRNAs in sera correlate with soluble biomarkers of immune activation but do not predict mortality in ART treated individuals with HIV-1 infection: A case control study

Introduction: The use of anti-retroviral therapy (ART) has dramatically reduced HIV-1 associated morbidity and mortality. However, HIV-1 infected individuals have increased rates of morbidity and mortality compared to the non-HIV-1 infected population and this appears to be related to end-organ diseases collectively referred to as Serious Non-AIDS Events (SNAEs). Circulating miRNAs are reported as promising biomarkers for a number of human disease conditions including those that constitute SNAEs. Our study sought to investigate the potential of selected miRNAs in predicting mortality in HIV-1 infected ART treated individuals. Materials and Methods: A set of miRNAs was chosen based on published associations with human disease conditions that constitute SNAEs. This case: control study compared 126 cases (individuals who died whilst on therapy), and 247 matched controls (individuals who remained alive). Cases and controls were ART treated participants of two pivotal HIV-1 trials. The relative abundance of each miRNA in serum was measured, by RTqPCR. Associations with mortality (all-cause, cardiovascular and malignancy) were assessed by logistic regression analysis. Correlations between miRNAs and CD4+ T cell count, hs-CRP, IL-6 and D-dimer were also assessed. Results: None of the selected miRNAs was associated with all-cause, cardiovascular or malignancy mortality. The levels of three miRNAs (miRs -21, -122 and -200a) correlated with IL-6 while miR-21 also correlated with D-dimer. Additionally, the abundance of miRs -31, -150 and -223, correlated with baseline CD4+ T cell count while the same three miRNAs plus miR- 145 correlated with nadir CD4+ T cell count. Discussion: No associations with mortality were found with any circulating miRNA studied. These results cast doubt onto the effectiveness of circulating miRNA as early predictors of mortality or the major underlying diseases that contribute to mortality in participants treated for HIV-1 infection